24 citations · 71 across the 8 of their papers we have counts for
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cs.CL2021
Measure and Improve Robustness in NLP Models: A Survey
Xuezhi Wang, Haohan Wang, Diyi Yang
As NLP models achieved state-of-the-art performances over benchmarks and gained wide applications, it has been increasingly important to ensure the safe deployment of these models…
cs.CL2021★ 17 cited
Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models
Tianlu Wang, Rohit Sridhar, Diyi Yang +1
Recently, NLP models have achieved remarkable progress across a variety of tasks; however, they have also been criticized for being not robust. Many robustness problems can be attr…
cs.CL2021★ 4 cited
Continual Learning for Text Classification with Information Disentanglement Based Regularization
Yufan Huang, Yanzhe Zhang, Jiaao Chen +2
Continual learning has become increasingly important as it enables NLP models to constantly learn and gain knowledge over time. Previous continual learning methods are mainly desig…